Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Prakash, P.; Gornale, Shivanand S.; Siddalingappa, Rashmi; Priya Title A name given to the resource Artificial Intelligence and Human-AI Driven Accreditation System for Higher Education Quality Assurance Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings ICECONF 2025 - 2025 2nd International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECONF65644.2025.11379639" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECONF65644.2025.11379639</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034731023?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034731023?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Prakash P., School of Mathematics and Computing Sciences, Rani Channamma University, Department of Computer Science, Karnataka, Belagavi, India; Gornale S.S., School of Mathematics and Computing Sciences, Rani Channamma University, Department of Computer Science, Karnataka, Belagavi, India; Siddalingappa R., Christ University, Dept. of Computer Science, Karnataka, Bengaluru, India; Priya, School of Engineering, Dayananda Sagar University, Dept. of Electronics and Communication Engineering, Karnataka, Bangalore, India Description An account of the resource This study introduces the Artificial Intelligence and Human-AI powered Accreditation System, which is meant to transform quality assurance in higher education. The suggested framework will combine human knowledge and smart automation to guarantee transparency, scalability and reliability of accreditation assessments. The data processing algorithm is based on Robust Principal Component Analysis of noise elimination and data normalization and then on the minimum-Redundancy Maximum-Relevancefeature selection algorithm. The fundamental classifier is a Graph Attention Network developed on PyTorch and PyTorch Geometric that is able to capture all the relations between institutional characteristics to make explainable judgments. A blockchain ledger is incorporated to record accreditation results to achieve security and traceability. The experimental simulations show excellent performance with high accuracy, evaluation times, and fairness than the traditional models. The system offers a strong, smart and open accreditation system that facilitates continuous improvement and accountability in the higher education system. 2025 IEEE. Subject The topic of the resource Artificial Intelligence; Blockchain-Based Quality Assurance; Graph Attention Network (GAT); Higher Education Accreditation; Human-AI Collaboration; mRMR Feature Selection; Robust PCA (RPCA) Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156070-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online